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dc.contributor.authorYang C
dc.contributor.authorPinart M
dc.contributor.authorKolsteren P
dc.contributor.authorVan Camp J
dc.contributor.authorDe Cock N
dc.contributor.authorNimptsch K
dc.contributor.authorPischon T
dc.contributor.authorLaird L
dc.contributor.authorPerozzi P
dc.contributor.authorCanali R
dc.contributor.authorHoge A
dc.contributor.authorStelmach-Mardas M
dc.contributor.authorOve Dragsted L
dc.contributor.authorPalombi SM
dc.contributor.authorDobre I
dc.contributor.authorBouwman J
dc.contributor.authorClarys P
dc.contributor.authorMinervini F
dc.contributor.authorDe Angelis M
dc.contributor.authorGobbetti M
dc.contributor.authorTafforeau J
dc.contributor.authorColtell O
dc.contributor.authorCorella D
dc.contributor.authorDe Ruyck H
dc.contributor.authorWalton J
dc.contributor.authorKehoe L
dc.contributor.authorMatthys C
dc.contributor.authorDe Baets B
dc.contributor.authorDe Tré G
dc.contributor.authorBronselaer A
dc.contributor.authorRivellese A
dc.contributor.authorGiacco R
dc.contributor.authorLombardo R
dc.contributor.authorDe Clercq S
dc.contributor.authorHulstaert N
dc.contributor.authorLachat C
dc.description.abstractPooled analysis of secondary data increases the power of research and enables scientific discovery in nutritional epidemiology. Information on study characteristics that determine data quality is needed to enable correct reuse and interpretation of data. This study aims to define essential quality characteristics for data from observational studies in nutrition. First, a literature review was performed to get an insight on existing instruments that assess the quality of cohort, case-control, and cross-sectional studies and dietary measurement. Second, 2 face-to-face workshops were organized to determine the study characteristics that affect data quality. Third, consensus on the data descriptors and controlled vocabulary was obtained. From 4884 papers retrieved, 26 relevant instruments, containing 164 characteristics for study design and 93 characteristics for measurements, were selected. The workshop and consensus process resulted in 10 descriptors allocated to "study design" and 22 to "measurement" domains. Data descriptors were organized as an ordinal scale of items to facilitate the identification, storage, and querying of nutrition data. Further integration of an Ontology for Nutrition Studies will facilitate interoperability of data repositories.en_US
dc.subjectData qualityen_US
dc.subjectObservational studyen_US
dc.subjectDietary assessmenten_US
dc.subjectNutritional epidemiologyen_US
dc.subjectData interoperabilityen_US
dc.titlePerspective: Essential study quality descriptors for data from nutritional epidemiologic researchen_US
dc.journal.titleAdvances in Nutrition

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